Then, for the comparison of two given neural networks, most of the known work focus on comparing the architecture and performance, for example [1]}, [2]}, [3]}.
The work in [4]}, [5]} focus on comparing the representation learnt by a neuron instead of the manifold formed by the activation vectors of the same type.
Instead, what's been proposed in our work is actually the comparison of the content learnt by two deep neural networks. We are aiming at finding the features and knowledge difference between two networks. The algorithm and criteria in our work can be generalized to the comparison between any two neural networks. Especially when two neural networks have the same input and similar accuracy, the curvature-based criteria can help in telling the detail of possible different learning methods of these networks.
